Sentence Similarity
sentence-transformers
PyTorch
ONNX
Safetensors
OpenVINO
xlm-roberta
mteb
Sentence Transformers
Eval Results (legacy)
text-embeddings-inference
Instructions to use Hiveurban/multilingual-e5-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Hiveurban/multilingual-e5-base with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Hiveurban/multilingual-e5-base") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
| import json | |
| import numpy as np | |
| from typing import List, Union | |
| def input_fn(input_data, content_type): | |
| data = json.loads(input_data) | |
| return data['inputs'] | |
| def predict_fn(data: Union[List[str], str], model): | |
| outputs = model(data, padding=False, truncation=True) | |
| embeddings = [np.array(r[0]).mean(axis=0).tolist() for r in outputs] | |
| return embeddings | |
| def output_fn(prediction, accept): | |
| return json.dumps( | |
| obj={ | |
| "outputs": prediction | |
| } | |
| ) | |